import os
import re

import cv2
import lmdb  # install lmdb by "pip install lmdb"
import numpy as np


def checkImageIsValid(imageBin):
    if imageBin is None:
        return False
    imageBuf = np.fromstring(imageBin, dtype=np.uint8)
    img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE)
    imgH, imgW = img.shape[0], img.shape[1]
    if imgH * imgW == 0:
        return False
    return True


def writeCache(env, cache):
    with env.begin(write=True) as txn:
        for k, v in cache.items():
            txn.put(k.encode(), v)


def _is_difficult(word):
    assert isinstance(word, str)
    return not re.match('^[\w]+$', word)


def createDataset(outputPath, imagePathList, labelList, lexiconList=None, checkValid=True):
    """
    Create LMDB dataset for CRNN training.
    ARGS:
        outputPath    : LMDB output path
        imagePathList : list of image path
        labelList     : list of corresponding groundtruth texts
        lexiconList   : (optional) list of lexicon lists
        checkValid    : if true, check the validity of every image
    """
    assert (len(imagePathList) == len(labelList))
    nSamples = len(imagePathList)
    env = lmdb.open(outputPath, map_size=1099511627776)
    cache = {}
    cnt = 1
    for i in range(nSamples):
        imagePath = imagePathList[i]
        label = labelList[i]
        if len(label) == 0:
            continue
        if not os.path.exists(imagePath):
            print('%s does not exist' % imagePath)
            continue
        with open(imagePath, 'rb') as f:
            imageBin = f.read()
        if checkValid:
            if not checkImageIsValid(imageBin):
                print('%s is not a valid image' % imagePath)
                continue

        imageKey = 'image-%09d' % cnt
        labelKey = 'label-%09d' % cnt
        cache[imageKey] = imageBin
        cache[labelKey] = label.encode()
        if lexiconList:
            lexiconKey = 'lexicon-%09d' % cnt
            cache[lexiconKey] = ' '.join(lexiconList[i])
        if cnt % 1000 == 0:
            writeCache(env, cache)
            cache = {}
            print('Written %d / %d' % (cnt, nSamples))
        cnt += 1
    nSamples = cnt - 1
    cache['num-samples'] = str(nSamples).encode()
    writeCache(env, cache)
    print('Created dataset with %d samples' % nSamples)


if __name__ == "__main__":
    data_dir = '/data/mkyang/datasets/English/benchmark/svtp/'
    lmdb_output_path = '/data/mkyang/datasets/English/benchmark_lmdbs_new/svt_p_645'
    gt_file = os.path.join(data_dir, 'gt.txt')
    image_dir = data_dir
    with open(gt_file, 'r') as f:
        lines = [line.strip('\n') for line in f.readlines()]

    imagePathList, labelList = [], []
    for i, line in enumerate(lines):
        splits = line.split(' ')
        image_name = splits[0]
        gt_text = splits[1]
        print(image_name, gt_text)
        imagePathList.append(os.path.join(image_dir, image_name))
        labelList.append(gt_text)

    createDataset(lmdb_output_path, imagePathList, labelList)
